Planning For Child-friendly Neighbourhoods In Hamilton: A Case Study
Bibliographic record
Abstract
Cities across the globe are experiencing increasing urbanization, and as a result, more and more children and youth are living in urban neighbourhoods. Neighbourhoods can provide opportunities for children to accumulate physical activity, which is one important indicator of healthy child development. However, auto-centric urban planning practices have contributed to an increasing reliance on parents to drive children to their destinations (Torres, 2009), a trend that is reflected in the low rate of Canadian children meeting daily physical activity guidelines (ParticipACTION, 2015). To support the healthy development of children and youth amidst the challenges of increasing urban densities, municipal governments are adopting the concept of child-friendly cities to build spaces that protect children’s rights to a healthy environment and to embrace policies in the creation of child-friendly neighbourhoods. The goal of this research paper was to evaluate the child-friendliness of the North End neighbourhood in Hamilton, Ontario to identify the built environment attributes that facilitate, or pose barriers to, children’s physical activity. To complete the analysis, this research involved a review of the literature linking the neighbourhood built environment to children’s physical activity, semi-structured interviews with key informants, an in-person neighbourhood audit, and a critical analysis of the locally focused planning documents that guide land use and development in the study area. Findings demonstrate that the North End is generally supportive of children’s physical activity; however, I identified several limitations of both the existing built environment and the land use policies and guidelines, which informed a set of recommendations to improve the child-friendliness of the neighbourhood overall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".